Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle

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dc.contributor.authorBennewitz, J.
dc.contributor.authorReinsch, N.
dc.contributor.authorGrohs, C.
dc.contributor.authorLeveziel, H.
dc.contributor.authorMalafosse, A.
dc.contributor.authorThomsen, H.
dc.contributor.authorXu, N.
dc.contributor.authorLooft, C.
dc.contributor.authorKuhn, C.
dc.contributor.authorBrockmann, G.
dc.contributor.authorSchwerin, M.
dc.contributor.authorWeimann, C.
dc.contributor.authorHiendleder, S.
dc.contributor.authorErhardt, G.
dc.contributor.authorMedjugorac, I.
dc.contributor.authorRuss, I.
dc.contributor.authorForster, M.
dc.contributor.authorBrenig, B.
dc.contributor.authorReinhardt, F.
dc.contributor.authorReents, R.
dc.contributor.authoret al.
dc.date.issued2003
dc.description.abstractA joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.
dc.description.statementofresponsibilityJörn Bennewitz, Norbert Reinsch, Cécile Grohs, Hubert Levéziel, Alain Malafosse, Hauke Thomsen, Ningying Xu, Christian Looft, Christa Kühn, Gudrun A. Brockmann, Manfred Schwerin, Christina Weimann, Stefan Hiendleder, Georg Erhardt, Ivica Medjugorac, Ingolf Russ, Martin Förster, Bertram Brenig, Fritz Reinhardt, Reinhard Reents, Gottfried Averdunk, Jürgen Blümel, Didier Boichard and Ernst Kalm
dc.identifier.citationGenetics Selection Evolution, 2003; 35(3):319-338
dc.identifier.doi10.1051/gse:2003011
dc.identifier.issn0999-193X
dc.identifier.issn1297-9686
dc.identifier.orcidHiendleder, S. [0000-0001-6222-3240]
dc.identifier.urihttp://hdl.handle.net/2440/36694
dc.language.isoen
dc.publisherE D P Sciences
dc.rights© INRA, EDP Sciences, 2003
dc.subjectQTL mapping
dc.subjectgranddaughter design
dc.subjectcombined analysis
dc.subjectQTL confirmation
dc.subjectdairy cattle
dc.titleCombined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle
dc.typeJournal article
pubs.publication-statusPublished

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